# Gabriel Kanev
> Source: https://gkanev.com/posts/tensorflow-docker-and-gpus-my-windows-11-nightmare-solved/
> Machine-readable version - 2026-04-07

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- Search ESC [Image: Docker TensorFlow GPU setup]
Two days. That’s how long it took before I stopped fighting Windows 11 and let Docker handle it instead.
## The Problem

Native TensorFlow GPU installation on Windows 11 is a mess. The official documentation recommends WSL2, but this approach created cascading issues:

NVIDIA driver incompatibilities with WSL2

- Permission problems accessing GPU resources

- Version mismatches between CUDA, cuDNN, and TensorFlow

- Environment breakage following Windows updates

I kept hitting errors like “Failed to get convolution algorithm” and missing CUDA library files - each attempted fix breaking something new.

## The Docker Solution

Rather than continue battling system-level configuration, I built a custom Docker container specifically for TensorFlow GPU development on Windows. The project is available on GitHub as TensorFlow-GPU-Docker-Setup.

Key features:

- Pre-configured GPU passthrough setup

- Comprehensive GPU testing scripts

- PyCharm integration fixes

- Detailed troubleshooting documentation

- Automated CUDA path configuration

## Implementation

The container builds from the TensorFlow GPU base image and includes NumPy, Pandas, and scikit-learn. Running it is straightforward:

docker build -t tensorflow-gpu-custom -f Dockerfile.gpu .
docker run --gpus all -it tensorflow-gpu-custom
## Why This Works

Data scientists should focus on their work, not system administration. By isolating dependencies within a container, the solution insulates your development environment from Windows updates and driver changes - the exact things that kept breaking everything.

Docker doesn’t fix the underlying TensorFlow Windows issues. It just means they stop being your problem.

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